Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, significantly hindering their usability. LUMIR brain registration challenge for Learn2Reg 2025 aims to advance the field by evaluating the performance of the registration on contrasts and modalities different from those included in the training set. Here we describe our submission to the challenge, which proposes a very simple idea for significantly improving robustness by transforming the images into MIND feature space before feeding them into the model. In addition, a special ensembling strategy is proposed that shows a small but consistent improvement.

翻译：近年来，基于深度学习的可变形配准方法日益普及。然而，这些方法在训练数据分布之外的泛化能力往往较差，严重限制了其实际应用。Learn2Reg 2025的LUMIR脑部配准挑战赛旨在通过评估模型在训练集未包含的对比度和模态上的配准性能，推动该领域的发展。本文介绍了我们针对该挑战赛提交的方案，其核心思想是通过将图像转换至MIND特征空间后再输入模型，显著提升配准的鲁棒性。此外，我们提出了一种特殊的集成策略，该方法虽改进幅度有限但具有稳定的提升效果。